线程池类似于多处理池？

Question

有工人的泳池课吗 线程, ，类似于多处理模块的 泳池类?

我喜欢例如平行地图功能的简单方法

def long_running_func(p):
    c_func_no_gil(p)

p = multiprocessing.Pool(4)
xs = p.map(long_running_func, range(100))

但是，我想在没有创建新流程的开销的情况下做到这一点。

我知道吉尔。但是，在我的用户酶中，该函数将是IO结合的C函数，Python包装器将在实际函数调用之前释放GIL。

我必须写自己的线程池吗？

Answer 1

我刚刚发现实际上 是 基于线程的池接口 multiprocessing 但是，模块有些隐藏，并且没有正确记录。

可以通过

from multiprocessing.pool import ThreadPool

它是使用包装Python线程的虚拟过程类实现的。这个基于线程的过程类可以在 multiprocessing.dummy 在 文档. 。据说，这个虚拟模块基于线程提供了整个多处理接口。

Answer 2

在Python 3中，您可以使用 concurrent.futures.ThreadPoolExecutor, ， IE：

executor = ThreadPoolExecutor(max_workers=10)
a = executor.submit(my_function)

看到 文档 有关更多信息和示例。

Answer 3

是的，它似乎具有（或多或少）相同的API。

import multiprocessing

def worker(lnk):
    ....    
def start_process():
    .....
....

if(PROCESS):
    pool = multiprocessing.Pool(processes=POOL_SIZE, initializer=start_process)
else:
    pool = multiprocessing.pool.ThreadPool(processes=POOL_SIZE, 
                                           initializer=start_process)

pool.map(worker, inputs)
....

Answer 4

对于非常简单且轻巧的东西（从 这里):

from Queue import Queue
from threading import Thread


class Worker(Thread):
    """Thread executing tasks from a given tasks queue"""
    def __init__(self, tasks):
        Thread.__init__(self)
        self.tasks = tasks
        self.daemon = True
        self.start()

    def run(self):
        while True:
            func, args, kargs = self.tasks.get()
            try:
                func(*args, **kargs)
            except Exception, e:
                print e
            finally:
                self.tasks.task_done()


class ThreadPool:
    """Pool of threads consuming tasks from a queue"""
    def __init__(self, num_threads):
        self.tasks = Queue(num_threads)
        for _ in range(num_threads):
            Worker(self.tasks)

    def add_task(self, func, *args, **kargs):
        """Add a task to the queue"""
        self.tasks.put((func, args, kargs))

    def wait_completion(self):
        """Wait for completion of all the tasks in the queue"""
        self.tasks.join()

if __name__ == '__main__':
    from random import randrange
    from time import sleep

    delays = [randrange(1, 10) for i in range(100)]

    def wait_delay(d):
        print 'sleeping for (%d)sec' % d
        sleep(d)

    pool = ThreadPool(20)

    for i, d in enumerate(delays):
        pool.add_task(wait_delay, d)

    pool.wait_completion()

为了支持任务完成的回调，您可以将回调添加到任务元组中。

Answer 5

嗨，要在Python中使用线程池，您可以使用此库：

from multiprocessing.dummy import Pool as ThreadPool

然后供使用，该库会喜欢：

pool = ThreadPool(threads)
results = pool.map(service, tasks)
pool.close()
pool.join()
return results

线程是您想要的线程数，任务是大多数映射到服务的任务列表。

Answer 6

这是我最终最终使用的结果。这是上面Dgorissen的类的修改版本。

文件： threadpool.py

from queue import Queue, Empty
import threading
from threading import Thread


class Worker(Thread):
    _TIMEOUT = 2
    """ Thread executing tasks from a given tasks queue. Thread is signalable, 
        to exit
    """
    def __init__(self, tasks, th_num):
        Thread.__init__(self)
        self.tasks = tasks
        self.daemon, self.th_num = True, th_num
        self.done = threading.Event()
        self.start()

    def run(self):       
        while not self.done.is_set():
            try:
                func, args, kwargs = self.tasks.get(block=True,
                                                   timeout=self._TIMEOUT)
                try:
                    func(*args, **kwargs)
                except Exception as e:
                    print(e)
                finally:
                    self.tasks.task_done()
            except Empty as e:
                pass
        return

    def signal_exit(self):
        """ Signal to thread to exit """
        self.done.set()


class ThreadPool:
    """Pool of threads consuming tasks from a queue"""
    def __init__(self, num_threads, tasks=[]):
        self.tasks = Queue(num_threads)
        self.workers = []
        self.done = False
        self._init_workers(num_threads)
        for task in tasks:
            self.tasks.put(task)

    def _init_workers(self, num_threads):
        for i in range(num_threads):
            self.workers.append(Worker(self.tasks, i))

    def add_task(self, func, *args, **kwargs):
        """Add a task to the queue"""
        self.tasks.put((func, args, kwargs))

    def _close_all_threads(self):
        """ Signal all threads to exit and lose the references to them """
        for workr in self.workers:
            workr.signal_exit()
        self.workers = []

    def wait_completion(self):
        """Wait for completion of all the tasks in the queue"""
        self.tasks.join()

    def __del__(self):
        self._close_all_threads()


def create_task(func, *args, **kwargs):
    return (func, args, kwargs)

使用游泳池

from random import randrange
from time import sleep

delays = [randrange(1, 10) for i in range(30)]

def wait_delay(d):
    print('sleeping for (%d)sec' % d)
    sleep(d)

pool = ThreadPool(20)
for i, d in enumerate(delays):
    pool.add_task(wait_delay, d)
pool.wait_completion()

Answer 7

创建新过程的开销很少，尤其是当其中仅4个时。我怀疑这是您应用程序的性能热点。保持简单，优化您所需的位置以及分析结果指向的位置。

Answer 8

没有内置的基于线程的池。但是，可以很快地实现生产者/消费者队列 Queue 班级。

从：https://docs.python.org/2/library/queue.html

from threading import Thread
from Queue import Queue
def worker():
    while True:
        item = q.get()
        do_work(item)
        q.task_done()

q = Queue()
for i in range(num_worker_threads):
     t = Thread(target=worker)
     t.daemon = True
     t.start()

for item in source():
    q.put(item)

q.join()       # block until all tasks are done